b'Adrian Sandu'

research

∙ 11/23/2022

A Two-Level Galerkin Reduced Order Model for the Steady Navier-Stokes Equations

We propose, analyze, and investigate numerically a novel two-level Galer...

0 Dylan Park, et al. ∙

research

∙ 10/21/2022

The Model Forest Ensemble Kalman Filter

Traditional data assimilation uses information obtained from the propaga...

0 Andrey A Popov, et al. ∙

research

∙ 07/14/2022

A Meta-learning Formulation of the Autoencoder Problem

A rapidly growing area of research is the use of machine learning approa...

0 Andrey A Popov, et al. ∙

research

∙ 05/06/2022

Physics-informed neural networks for PDE-constrained optimization and control

A fundamental problem of science is designing optimal control policies t...

0 Jostein Barry-Straume, et al. ∙

research

∙ 01/20/2022

Eliminating Order Reduction on Linear, Time-Dependent ODEs with GARK Methods

When applied to stiff, linear differential equations with time-dependent...

0 Steven Roberts, et al. ∙

research

∙ 11/27/2021

Ensemble Variational Fokker-Planck Methods for Data Assimilation

Particle flow filters that smoothly transform particles from being sampl...

0 Amit N Subrahmanya, et al. ∙

research

∙ 11/16/2021

Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation

The data assimilation procedures used in many operational numerical weat...

0 Austin Chennault, et al. ∙

research

∙ 09/20/2021

A Stochastic Covariance Shrinkage Approach to Particle Rejuvenation in the Ensemble Transform Particle Filter

Rejuvenation in particle filters is necessary to prevent the collapse of...

0 Andrey A Popov, et al. ∙

research

∙ 08/27/2021

Investigation of Nonlinear Model Order Reduction of the Quasigeostrophic Equations through a Physics-Informed Convolutional Autoencoder

Reduced order modeling (ROM) is a field of techniques that approximates ...

0 Rachel Cooper, et al. ∙

research

∙ 03/06/2021

Symplectic GARK methods for Hamiltonian systems

Generalized Additive Runge-Kutta schemes have shown to be a suitable too...

0 Michael Günther, et al. ∙

research

∙ 03/01/2021

A unified formulation of splitting-based implicit time integration schemes

Splitting-based time integration approaches such as fractional steps, al...

0 Severiano González-Pinto, et al. ∙

research

∙ 02/25/2021

Multifidelity Ensemble Kalman Filtering using surrogate models defined by Physics-Informed Autoencoders

The multifidelity ensemble Kalman filter aims to combine a full-order mo...

0 Andrey A Popov, et al. ∙

research

∙ 02/19/2021

Multirate Linearly-Implicit GARK Schemes

Many complex applications require the solution of initial-value problems...

0 Michael Guenther, et al. ∙

research

∙ 11/21/2020

Linearly Implicit Multistep Methods for Time Integration

Time integration methods for solving initial value problems are an impor...

0 Ross Glandon, et al. ∙

research

∙ 11/07/2020

A fast time-stepping strategy for ODE systems equipped with a surrogate model

Simulation of complex dynamical systems arising in many applications is ...

0 Steven Roberts, et al. ∙

research

∙ 08/04/2020

Linearly implicit GARK schemes

Systems driven by multiple physical processes are central to many areas ...

0 Adrian Sandu, et al. ∙

research

∙ 07/01/2020

A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates

This work develops a new multifidelity ensemble Kalman filter (MFEnKF) a...

0 Andrey A Popov, et al. ∙

research

∙ 04/08/2020

Convergence Results for Implicit–Explicit General Linear Methods

This paper studies fixed-step convergence of implicit-explicit general l...

0 Adrian Sandu, et al. ∙

research

∙ 03/29/2020

An Explicit Probabilistic Derivation of Inflation in a Scalar Ensemble Kalman Filter for Finite Step, Finite Ensemble Convergence

This paper uses a probabilistic approach to analyze the converge of an e...

0 Andrey A Popov, et al. ∙

research

∙ 02/29/2020

A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering

Operational Ensemble Kalman Filtering (EnKF) methods rely on model-speci...

0 Andrey A Popov, et al. ∙

research

∙ 02/03/2020

Parallel implicit-explicit general linear methods

High-order discretizations of partial differential equations (PDEs) nece...

0 Steven Roberts, et al. ∙

research

∙ 01/23/2020

Goal-oriented a posteriori estimation of numerical errors in the solution of multiphysics systems

This paper develops a general methodology for a posteriori error estimat...

0 Mahesh Narayanamurthi, et al. ∙

research

∙ 12/02/2019

Efficient implementation of partitioned stiff exponential Runge-Kutta methods

Multiphysics systems are driven by multiple processes acting simultaneou...

0 Mahesh Narayanamurthi, et al. ∙

research

∙ 10/30/2019

Implicit multirate GARK methods

This work considers multirate generalized-structure additively partition...

0 Steven Roberts, et al. ∙

research

∙ 10/06/2019

Adaptive Krylov-Type Time Integration Methods

The Rosenbrock-Krylov family of time integration schemes is an extension...

0 Paul Tranquilli, et al. ∙

research

∙ 08/28/2019

Biorthogonal Rosenbrock-Krylov time discretization methods

Many scientific applications require the solution of large initial-value...

0 Ross Glandon, et al. ∙

research

∙ 08/26/2019

Partitioned Exponential Methods for Coupled Multiphysics Systems

Multiphysics problems involving two or more coupled physical phenomena a...

0 Mahesh Narayanamurthi, et al. ∙

research

∙ 02/02/2019

Alternating Directions Implicit Integration in a General Linear Method Framework

Alternating Directions Implicit (ADI) integration is an operator splitti...

0 Arash Sarshar, et al. ∙

research

∙ 01/14/2019

ODE Test Problems: a MATLAB suite of initial value problems

ODE Test Problems (OTP) is an object-oriented MATLAB package offering a ...

0 Steven Roberts, et al. ∙

research

∙ 11/30/2018

Coupled Multirate Infinitesimal GARK Schemes for Stiff Systems with Multiple Time Scales

Differential equations derived from many real-world applications are dom...

0 Steven Roberts, et al. ∙

research

∙ 09/24/2018

A Bayesian Approach to Multivariate Adaptive Localization in Ensemble-Based Data Assimilation with Time-Dependent Extensions

Ever since its inception, the Ensemble Kalman Filter has elicited many h...

0 Andrey A Popov, et al. ∙

research

∙ 08/07/2018

A Class of Multirate Infinitesimal GARK Methods

Differential equations arising in many practical applications are charac...

0 Adrian Sandu, et al. ∙

research

∙ 04/20/2018

Design of High-Order Decoupled Multirate GARK Schemes

Multirate time integration methods apply different step sizes to resolve...

0 Arash Sarshar, et al. ∙

research

∙ 02/20/2018

A Learning Based Approach for Uncertainty Analysis in Numerical Weather Prediction Models

Complex numerical weather prediction models incorporate a variety of phy...

0 Azam Moosavi, et al. ∙

research

∙ 01/02/2018

A Machine Learning Approach to Adaptive Covariance Localization

Data assimilation plays a key role in large-scale atmospheric weather fo...

0 Azam Moosavi, et al. ∙

research

∙ 04/19/2017

DATeS: A Highly-Extensible Data Assimilation Testing Suite v1.0

A flexible and highly-extensible data assimilation testing suite, named ...

0 Ahmed Attia, et al. ∙

research

∙ 04/19/2017

DATeS: A Highly-Extensible Data Assimilation Testing Suite

A flexible and highly-extensible data assimilation testing suite, named ...

0 Ahmed Attia, et al. ∙

research

∙ 04/09/2017

Solving Parameter Estimation Problems with Discrete Adjoint Exponential Integrators

The solution of inverse problems in a variational setting finds best est...

0 Ulrich Roemer, et al. ∙

research

∙ 01/23/2017

EPIRK-W and EPIRK-K time discretization methods

Exponential integrators are special time discretization methods where th...

0 Mahesh Narayanamurthi, et al. ∙

research

∙ 01/13/2017

Multivariate predictions of local reduced-order-model errors and dimensions

This paper introduces multivariate input-output models to predict the er...

0 Azam Moosavi, et al. ∙

research

∙ 11/05/2015

Robust data assimilation using L_1 and Huber norms

Data assimilation is the process to fuse information from priors, observ...

0 Vishwas Rao, et al. ∙

Adrian Sandu

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